Basic data

Kable is a very simple table generator that takes input as either a matrix or data frame (Zhu 2018); see the Kable documentation for styling options available.

In this template the kable formatting is applied automatically when using the chunk option chunk option “table = T”. By default, this template uses the hover style with full_width = FALSE. (Technical info: automatic formatting is implemented by kintr::knit_hooks() (Xie 2018) via the chunk source and optimal results are achieved when including the option results = “hide”. For the same reason, eval = F is included so that the chunk is not evaluated twice. The source code cannot be shown when the kable formatting is automatically applied.)

For example:

```{r, table = T, results = “hide”, eval = F}
  head(iris)
```


will output:

If custom table options are needed, then the kable formatting can be applied as normal:

head(iris) %>%
  kable("html") %>%
  kable_styling("hover", full_width = TRUE)

Model output

The broom::tidy (Robinson and Hayes 2018) function may be used to convert the output into a data frame that is compatible with kable When data is supplied from a model, which may be more appropraite for a report or publication.

For example, the summary output of a linear mixed effects model (lmr4::lmer) looks like:

model <- lmer(Sepal.Length ~ Sepal.Width + Petal.Width + (1|Species), data = iris)

summary(model)

Linear mixed model fit by REML ['lmerMod']
Formula: Sepal.Length ~ Sepal.Width + Petal.Width + (1 | Species)
   Data: iris

REML criterion at convergence: 190.6

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.78885 -0.55571 -0.05715  0.44177  2.85990 

Random effects:
 Groups   Name        Variance Std.Dev.
 Species  (Intercept) 0.3059   0.5531  
 Residual             0.1889   0.4346  
Number of obs: 150, groups:  Species, 3

Fixed effects:
            Estimate Std. Error t value
(Intercept)   3.2551     0.4597   7.082
Sepal.Width   0.6504     0.1152   5.645
Petal.Width   0.5000     0.1783   2.804

Correlation of Fixed Effects:
            (Intr) Spl.Wd
Sepal.Width -0.576       
Petal.Width -0.151 -0.410

Broom defines tidying methods for extracting three kinds of statistics from an object (see here):

• tidy(): component-level statistics
• augment(): observation-level statistics
• glance(): model-level statistics

tidy():

glance():

augment():

Colour schemes

There are three colour schemes built into this theme: scale_colour_telethonkids(“light”), scale_colour_telethonkids(“dark”) and scale_colour_telethonkids(“grey”). These colour themes are implemented by adding the appropriate theme to the ggplot code:

ggplot(iris, aes(Petal.Width, Petal.Length, color = Species)) +
  labs(title = "Iris Dataset", x = "Petal Width", y = "Petal Length") +
  geom_point(size = 4) +
  scale_color_telethonkids("light") +
  theme_classic()

A continuous colour theme can be implemented by adding the argument discrete = FALSE the scale_colour_telethonkids():

ggplot(iris, aes(Petal.Width, Petal.Length, color = Petal.Length)) +
  geom_point(size = 4, alpha = .6) +
  scale_color_telethonkids(discrete = FALSE, palette = "dark") +
  theme_classic()

Bar plots can also be ‘filled’ by discrete variables with differnt palettes by using scale_fill_telethonkids(palette = “mixed”, guide = “none”).

To ensure the image is responsive on different sized screens, the code out.extra = “figure” should be included in the R chunk options (this calls an option hook defined in hooks.R).